Monthly Archives: October 2017

Our group was featured in this New Yorker article, showcasing Rebecca Pankow and John Oberlin’s work programming Baxter to pick petals from a daisy, as well as some of my thoughts on inequality and automation. I was thrilled with Sheelah’s work on this very important issue, focusing on the effects of automation and our changing economy.

There are many tasks that are too dangerous for humans to perform that would be better suited for a robot, such as defusing a bomb or repairing a nuclear reactor. Ideally, these robots would be autonomous, but currently, robots are not able to perform all tasks on their own yet. For robots to help with these problems today, they are directly controlled from afar by a human user, in an act called teleoperation. With this work, we set out to develop a teleoperation interface that is as intuitive and efficient as possible for completing the task.

We developed a virtual reality interface to allow novice users to efficiently teleoperate a robot and view it’s environment in 3D. We have released an open-source ROS package, ROS Reality, which allows anyone to connect a ROS network to a Unity scene over the internet via websockets. ROS topics can be sent to the Unity scene, and data from the Unity scene can be sent to the ROS network as a topic. This allows a human to perceive a scene and teleoperate the robot in it to perform a complex task, such as picking up a cup, as simply as they would in real life. We conducted a user study to compare the speed of our interface to traditional teleoperation methodologies, such as keyboard and monitor, and found a 66% increase in task completion under our system.

Below is a video of our system being used to teleoperate a Baxter robot at MIT from Brown University (41 miles away). Since our bandwidth requirements are about the same as a Skype call, we are able to establish a relatively low-latency connection that allows 12 cups to be easily stacked in a row. For more information, please check out our paper, which was accepted to ISRR 2017!

When humans are collaborating together, they are constantly communicating their intents through body language and speech in order to arrive at a common understanding. Similarly, we would like to enable robots to be able to communicate their intents so that robots and humans can quickly converge to solving the same problem and avoid miscommunication issues. A particularly important intent for robots to communicate to humans is motion, because of the safety concerns involved with a robot actuating in close-quarters with a human. Human-robot collaboration would be safer and more efficient if robots could communicate their motion intent to human partners in a quick and easy manner.

In our latest paper, we propose a mixed-reality head-mounted display interface that allows visualization of a robot’s future poses over the wearer’s real-world view of the environment. This allows a human to view the entire planned motion of the robot in the real workspace before it even moves, removing any potential issues of testing a real motion plan in the environment. To measure our interface’s ability to improve collaboration speed and accuracy, we conducted an experiment with real world users to compare our interface to traditional and no visualization techniques. We found our interface increased accuracy by 16% and a 62% decrease in the task completion compared to traditional visualizations.

If you’d like to see a demo of the interface yourself, check out the video below! Watch as the robot visualizes different motion plans that attempts to move the arm from one side of the table to the other without hitting any of the blocks. As opposed to traditional monitor and keyboard interfaces, our MR headset allows users to inspect the real scene quickly and efficiently. The code for the system is available on Github here. For more information, see our paper, which was accepted into ISRR 2017!